The popularity of Amazon's EC2 cloud platform has increased in commercial and scientific high-performance computing (HPC) applications domain in recent years. However, many HPC users consider dedicated high-performance clusters, typically found in large compute centers such as those in national laboratories, to be far superior to EC2 because of significant communication overhead of the latter. We find this view to be quite narrow and the proper metrics for comparing high-performance clusters to EC2 is turnaround time and cost. In this work, we first compare the HPC-grade EC2 cluster to top-of-the-line HPC clusters based on turnaround time and total cost of execution. When measuring turnaround time, we include expected queue wait time on HPC clusters. Our results show that although as expected, standard HPC clusters are superior in raw performance, they suffer from potentially significant queue wait times. We show that EC2 clusters may produce better turnaround times due to typically lower wait queue times. To estimate cost, we developed a pricing model---relative to EC2's node-hour prices---to set node-hour prices for (currently free) HPC clusters. We observe that the cost-effectiveness of running an application on a cluster depends on raw performance and application scalability. However, despite the potentially lower queue wait and turnaround times, the primary barrier to using clouds for many HPC users is the cost. Amazon EC2 provides a fixed-cost option (called on-demand) and a variable-cost, auction-based option (called the spot market). The spot market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost by exploiting redundancy in the EC2 spot market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to 7x cheaper than using the on-demand market and up to 44% cheaper than the best non-redundant, spot-market algorithm. Finally, we extend our adaptive algorithm to exploit several opportunities for cost-savings on the EC2 spot market. First, we incorporate application scalability characteristics into our adaptive policy. We show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56% cost-savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale. Second, we demonstrate potential for obtaining considerable free computation time on the spot market enabled by its hour-boundary pricing model.

The advances in computing and communication technologies and software tools have resulted in an explosive growth in networked applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. In a similar way, the underlying information infrastructure, e.g. the Internet, is large, complex, heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources. The combination of the two results in application development and management complexities that break current computing paradigms, which are based on static behaviors. As a result, applications, programming environments and information infrastructures are rapidly becoming fragile, unmanageable and insecure. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems. Autonomic programming paradigm is inspired by the human autonomic nervous system that handles complexity, uncertainties and abnormality. The overarching goal of the autonomic programming paradigm is to help building systems and applications capable of self-management. Firstly, we investigated the large-scale scientific computing applications which generally experience different execution phases at run time and each phase has different computational, communication and storage requirements as well as different physical characteristics. In this dissertation, we present Physics Aware Optimization (PAO) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Optimization Manager to exploit the PAO paradigm. On the other hand we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring server and networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means it can be self-managed component) by using our autonomic programming paradigm. Our POA technique for medical application yielded about 3X improvement of performance with 98.3% simulation accuracy compared to traditional techniques for performance optimization. Also, our Self-configuration management for power and performance management in GPU cluster demonstrated 53.7% power savings for CUDAworkload while maintaining the cluster performance within given acceptable thresholds.

In the quest for improving building energy efficiency raising the level of performance of the building enclosure has become critical. As the thermal performance of the building enclosure improves so does the overall energy efficiency of the building. One key component in determining the energy performance of the building enclosure is windows. Windows have an integral role in determining the energy performance of a building by allowing light and heat from the sun to enter into a space. Energy efficient buildings take advantage of this free solar energy to help offset heating energy consumption and electric lighting loads. However, windows are traditionally the least insulating component of the modern building assembly. With excessive use, larger window areas can lead to greater occupant discomfort and energy consumption from greater night-time heat loss, higher peak and total cooling energy demand from unwanted solar gains, and discomfort glare. As a result, windows must be carefully designed to not only minimize heat loss, but also effectively control solar gains to maintain both a thermally and visually comfortable environment for the appropriate climate region and orientation. In this thesis, a complete analysis of window assemblies for commercial office buildings is presented. The analysis is divided into three sections: the Insulated Glazing Unit (IGU), the Curtain Wall Section (frames), and the overall energy performance of a typical office building. The first section investigates the performance characteristics of typical and high performance IGUs, specifically its insulating value (Ucg), its solar heat gain properties (Solar Heat Gain Coefficient, SHGC), and its visual transmittance (VT) through one-dimensional heat transfer and solar-optical modeling. Mechanisms of heat transfer across IGUs were investigated giving insight into the parameters that had the most significant effect on improving each performance characteristic. With a through understanding of IGU performance, attainable performance limits for each of property were generated from combining of different glazing materials, fill gases, and coatings. Through the right combination of materials IGU performance can be significantly altered. The U-value performance of IGUs ranges from 2.68 W/m2K (R-2.1) for a double-glazed, clear, air filled IGU to 0.27 W/m2K (R-21) for a quint-glazed, low-E, xenon filled high performance IGU. The second part of the thesis looks at the thermal performance of curtain wall sections that hold the IGU through two-dimensional heat transfer modeling. Similar to the IGUs, heat transfer mechanisms were studied to by substituting different materials to determine which components are crucial to thermal performance. From this analysis improvements were made to typical curtain wall design that significantly reduces the overall heat transfer within the frame section, producing a high performance curtain wall section. With simple modifications, a high performance curtain wall section can reduce its U-value by as much as 81% over a typical curtain wall section, going from 13.39 W/m2K to 2.57 W/m2K. Thus significantly reducing the U-value of curtain wall systems, particularly for smaller windows. The final part of the thesis examines the impact of typical and high performance windows on the energy performance of perimeter offices of a high-rise commercial building located in Southern Ontario. An hourly simulation model was set up to evaluate both the annual and peak energy consumption of a typical perimeter office space. The office faced the four cardinal directions of north, east, south, and west to evaluate the effect of orientation. The model also included continuous dimming lighting controls to make use of the available daylight. The effect of exterior shading on perimeter space energy performance was also investigated with both dynamic and static exterior shading devices. The results of the simulations revealed that window properties have very little influence on the energy performance of a high internal heat gain office, that is typical of older offices with less energy efficient office equipment and lighting and a higher occupant density. Conversely, window properties, particularly the insulating value of the window, has a greater effect on the energy performance of a mid to low internal heat gain office that is typical of most modern day commercial buildings. The results show windows with lower U-values yet higher SHGC are preferred over windows of similar U-values but with lower SHGC. The results also indicate that both static and dynamic shading have very little effect on energy performance of mid to low internal heat gain offices. From this analysis optimal window areas in the form of window-to-wall ratios (WWR) are presented for each orientation for mid to low internal heat gain offices. The optimal WWR for south-facing facades are between 0.50 to 0.66, and 0.30 to 0.50 for east-, west-, and north-facing facades, while for high internal heat gain perimeter spaces window areas should be kept to a minimum.

High-Performance Computing (HPC) resources, such as Supercomputers, Clusters, Grids and HPC Clouds, are managed by Resource Management Systems (RMSs) that multiple resources among multiple users and decide how computing nodes are allocated to user applications. As more and more petascale computing resources are built and exascale is to be achieved by 2020, optimizing resource allocation to applications is critical to ensure their efficient execution. However, current RMSs, such as batch schedulers, only offer a limited interface. In most cases, the application has to blindly choose resources at submittal without being able to adapt its choice to the state of the target resources, neither before it started nor during execution. The goal of this Thesis is to improve resource management, so as to allow applications to efficiently allocate resources. We achieve this by proposing software architectures that promote collaboration between the applications and the RMS, thus, allowing applications to negotiate the resources they run on. To this end, we start by analysing the various types of applications and their unique resource requirements, categorizing them into rigid, moldable, malleable and evolving. For each case, we highlight the opportunities they open up for improving resource management.The first contribution deals with moldable applications, for which resources are only negotiated before they start. We propose CooRMv1, a centralized RMS architecture, which delegates resource selection to the application launchers. Simulations show that the solution is both scalable and fair. The results are validated through a prototype implementation deployed on Grid'5000. Second, we focus on negotiating allocations on geographically-distributed resources, managed by multiple institutions. We build upon CooRMv1 and propose distCooRM, a distributed RMS architecture, which allows moldable applications to efficiently co-allocate resources managed by multiple independent agents. Simulation results show that distCooRM is well-behaved and scales well for a reasonable number of applications. Next, attention is shifted to run-time negotiation of resources, so as to improve support for malleable and evolving applications. We propose CooRMv2, a centralized RMS architecture, that enables efficient scheduling of evolving applications, especially non-predictable ones. It allows applications to inform the RMS about their maximum expected resource usage, through pre-allocations. Resources which are pre-allocated but unused can be filled by malleable applications. Simulation results show that considerable gains can be achieved. Last, production-ready software are used as a starting point, to illustrate the interest as well as the difficulty of improving cooperation between existing systems. GridTLSE is used as an application and DIET as an RMS to study a previously unsupported use-case. We identify the underlying problem of scheduling optional computations and propose an architecture to solve it. Real-life experiments done on the Grid'5000 platform show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, it is shown that the solution is scalable.

In natural dendroflora of Lithuania willow genus (Salix L.) is abundant in species amount. Its bark contains active materials which are known as possessing anti-inflammatory properties since former times. Bark extracts are contained in analgesic as well as antirheumatic preparations. Its therapeutic effectiveness is associated with salicine which turns into salicylic acid. Willow bark is a know phytotherapeutic precursor of aspirin. To use more natural and not synthetic materials in the 21st century the methods for extraction of active materials for medicine are being sought. Thus, the studies on plant groups possessing the most intense anti-inflammatory properties became urgent. The amount of salicin varies among different species. Acording to the European Pharmacopoea, minimal amount of 1,5% of salicin is required in Willow bark. The aim of the study was to investigate salicylates amount variation in willow bark of different Salix species growing in Lithuania. The studies were carried out using the routine methods of pharmacopeia. After extraction analysis was performed by high performance liquid chromatography (HPLC) method. The first step of study was made among 3 samples of each plant, colected at different growing period (1-st year autumn, 2-nd year autumn and 2-nd year spring). Comparison of the quantities of the salicylic derivatives depending upon age of the plant and time of collection shows, that plants at two years of growth and collected in autumn accumulate... [to full text] / Natūralioje Lietuvos floroje gluosnių (Salix L.) rūšys yra plačiai paplitusios. Gluosnio žievė – nuo seno žinoma kaip priešuždegiminiu poveikiu pasižyminti vaistinė augalinė žaliava. Jos ekstraktai įeina į priešreumatinių ir analgezinių preparatų sudėtį. Terapinis veikimas yra susijęs su salicinu, kuris organizme virsta salicilo rūgštimi. Gluosnio žievė yra fitoterapinis aspirino pirmtakas. XXI amžiuje vis didesnis dėmesys krypsta į natūralius, o ne sintetinius preparatus. Tokiu būdu, augalų, pasižyminčių terapinėmis savybėmis tyrimai įgyja didelę reikšmę. Skirtingų rūšių gluosnių žievėje, kaupiamas salicino kiekis skiriasi. Europos farmakopėjoje reglamentuojamas minimalus salicino kiekis yra 1,5%. Šio tyrimo tikslas buvo Ištirti Lietuvoje augančių gluosnio rūšių salicilatų kiekio įvairavimą vaistinėje augalinėje žaliavoje (žievėje). Darbo uždaviniai- atlikti įvairių Lietuvoje augančių gluosnio rūšių salicilatų kiekio analizę, palyginti salicilatų kiekį gluosnio žievėje, atsižvelgiant į augalo amžių bei rinkimo laiką bei įvertinti gautus rezultatus, numatomą terapinį veikimą. Tyrimai atlikti naudojant farmakopėjinius analizės metodus. Žievės ekstraktai buvo tiriami efektyviosios skysčių chromatografijos metodu.Šio tyrimo pirmo etapo metu išanalizuota 12 gluosnių taksonų. Buvo ištirti vienerių ir dviejų metų, taip pat pavasarį ir rudenį rinkti žievės pavyzdžiai. Didesni kiekiai aktyvių medžiagų rasta rudenį ir 2 metų amžiaus gluosnių žievės pavyzdžiuose. Antrajame tyrimo... [toliau žr. visą tekstą]

The CellPilot library provides a uniform communication programming model, based on Pilot's process/channel approach, for clusters of Cell Broadband Engine processors. Pilot, a thin layer on top of the Message Passing Interface (MPI) library, allows processes to read/write messages on channels defined between pairs of processes on the cluster, but Pilot alone does not help a Cell programmer cope with the considerable complexities of intra-Cell communication. With CellPilot, programmers still design software in terms of processes, but they can now be located on a Cell node's Power Processor Elements (PPEs), Synergistic Processing Elements (SPEs), or non-Cell node within a heterogeneous Cell cluster, and communication is accomplished via channels between process pairs. Programs are coded in terms of reading and writing on those channels, whereupon CellPilot transparently applies whichever communication mechanisms are required to transport the message, regardless of its endpoints. This gives the programmer a way to handle inter-process communication while avoiding low-level I/O operations and the use of multiple libraries.

Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data management systems for Big Data. Our first and second contributions address the challenge of providing efficient support for Big Volume of data in data-intensive high performance computing (HPC) environments. Particularly, we address the shortcoming of existing approaches to handle atomic, non-contiguous I/O operations in a scalable fashion. We propose and implement a versioning-based mechanism that can be leveraged to offer isolation for non-contiguous I/O without the need to perform expensive synchronizations. In the context of parallel array processing in HPC, we introduce Pyramid, a large-scale, array-oriented storage system. It revisits the physical organization of data in distributed storage systems for scalable performance. Pyramid favors multidimensional-aware data chunking, that closely matches the access patterns generated by applications. Pyramid also favors a distributed metadata management and a versioning concurrency control to eliminate synchronizations in concurrency. Our third contribution addresses Big Volume at the scale of the geographically distributed environments. We consider BlobSeer, a distributed versioning-oriented data management service, and we propose BlobSeer-WAN, an extension of BlobSeer optimized for such geographically distributed environments. BlobSeer-WAN takes into account the latency hierarchy by favoring locally metadata accesses. BlobSeer-WAN features asynchronous metadata replication and a vector-clock implementation for collision resolution. To cope with the Big Velocity characteristic of Big Data, our last contribution feautures DStore, an in-memory document-oriented store that scale vertically by leveraging large memory capability in multicore machines. DStore demonstrates fast and atomic complex transaction processing in data writing, while maintaining high throughput read access. DStore follows a single-threaded execution model to execute update transactions sequentially, while relying on a versioning concurrency control to enable a large number of simultaneous readers.

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Using a model system of glycosylated and aglycon standards consisting of rutin and quercetin respectively, and a series of pre-packed solid phase extraction cartridges, including C18 Extra-Clean, DSC-18, DPA-6S, Oasis HLB and Amberlite XAD-2. The experimental findings also showed that use of a commercial hesperinidase preparation, resulted in adequate hydrolysis of the glycosylated standard rutin. Based on these findings, the phenolic compounds and flavonoids from maple sap and syrup were separated using the Amberlite XAD-2 column, where the glycosylated fractions eluted with 60% aqueous methanol solution and the aglycon fractions eluted with a methanol:acetonitrile mixture (1:1, v/v). The recovered glycosylated fractions were subjected to enzymatic hydrolysis using the hesperinidase preparation and the liberated phenolic compounds and flavonoids, as well as the sugar components were analyzed by high-performance liquid chromatography (HPLC).

Comparative high-performance liquid chromatography (HPLC) and gas-liquid chromatography (GC) analyses of selected phenolic and flavonoid standards were developed using a wide range of detectors, including ultraviolet diode-array (UV-DAD) and electrochemical (EC) detectors for HPLC and flame ionization detector (FID) and mass spectrometry (MS) for GC. The results demonstrated that the limits of detection obtained with HPLC-EC analysis were 10 to 500-times higher for phenolic acid standards and 2 to 50-times higher for flavonoid standards than those obtained with the HPLC-UV analysis. HPLC-EC was more sensitive than GC/FID for all investigated standards, especially for vanillin and syringaldehyde. The results indicated that GC/FID/MS analysis of phenolic and flavonoid standards was more efficient than that of HPLC, providing a fast analysis with better resolution and baseline separation of all standards with minimum co-elution. The only co-elution encountered in GC/FID was with coniferol and p-coumaric acids. For HPLC analysis, (-)-epicatechin, caffeic and homovanillic acids were co-eluted at 28.04 min and sinapic and ferulic acids at 34.57 min. Phenolic compounds and flavonoids were extracted from maple sap and maple syrup with ethyl acetate and the recovered compounds were subjected to HPLC and GC analyses. Tentative identification of phenolic compounds and flavonoids in maple sap and maple syrup indicated the presence of protocatechuic acid, hydroxycinnamic acid derivatives, (+)-catechin, (-)-epicatechin, vanillin, coniferol, syringaldehyde, flavanols and dihydroflavonols related compounds. In addition, the identification by GC/MS of protocatechuic acid, vanillin, syringaldehyde, coniferol and p-coumaric acid was made by comparing mass spectrum characteristics of individual peak from total ion chromatogram (TIC) to that of standard compounds. The seasonal variation of selected phenolic compounds and flavonoids present in maple sap and maple syrup was also invest